# Explainable, automated urban interventions to improve pedestrian and vehicle safety

arXiv: https://arxiv.org/abs/2110.11672 · HTML: https://mlsanity.com/paper/2110.11672/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 83 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Attention Branch Network: Learning of Attention Mechanism for Visual Explanation](https://mlsanity.com/paper/1812.10025/) (2018)
- [Predicting the Driver's Focus of Attention: the DR(eye)VE Project](https://mlsanity.com/paper/1705.03854/) (2017)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1908.02686/) (2019)
- [Is Faster R-CNN Doing Well for Pedestrian Detection?](https://mlsanity.com/paper/1607.07032/) (2016)
- [U-CAM: Visual Explanation using Uncertainty based Class Activation Maps](https://mlsanity.com/paper/1908.06306/) (2019)
- [How Far are We from Solving Pedestrian Detection?](https://mlsanity.com/paper/1602.01237/) (2016)
- [Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale](https://mlsanity.com/paper/1704.02965/) (2017)
- [A deep learning approach for detecting traffic accidents from social media data](https://mlsanity.com/paper/1801.01528/) (2018)
- [Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation](https://mlsanity.com/paper/1703.10757/) (2017)
- [Random Walks on Multiplex Networks](https://mlsanity.com/paper/1306.0519/) (2013)
- [The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City](https://mlsanity.com/paper/1407.1031/) (2014)
- [FARSA: Fully Automated Roadway Safety Assessment](https://mlsanity.com/paper/1901.06013/) (2019)
- [Where are the Dangerous Intersections for Pedestrians and Cyclists: A Colocation-Based Approach](https://mlsanity.com/paper/2006.03131/) (2020)

## Built on by (most cited first, 3 of 3)

- [Sensing accident-prone features in urban scenes for proactive driving and accident prevention](https://mlsanity.com/paper/2202.12788/) (2022)
- [Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model](https://mlsanity.com/paper/2302.07440/) (2023)
- [Predicting the impact of urban change in pedestrian and road safety](https://mlsanity.com/paper/2202.01781/) (2022)

## Abstract (arXiv)

At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e.g. the problems of congestion and pollution. And yet, we can not disregard the most vulnerable elements in the urban landscape: pedestrians, exposed to higher risks than other road users. Indeed, safe, accessible, and sustainable transport systems in cities are a core target of the UN's 2030 Agenda. Thus, there is an opportunity to apply advanced computational tools to the problem of traffic safety, in regards especially to pedestrians, who have been often overlooked in the past. This paper combines public data sources, large-scale street imagery and computer vision techniques to approach pedestrian and vehicle safety with an automated, relatively simple, and universally-applicable data-processing scheme. The steps involved in this pipeline include the adaptation and training of a Residual Convolutional Neural Network to determine a hazard index for each given urban scene, as well as an interpretability analysis based on image segmentation and class activation mapping on those same images. Combined, the outcome of this computational approach is a fine-grained map of hazard levels across a city, and an heuristic to identify interventions that might simultaneously improve pedestrian and vehicle safety. The proposed framework should be taken as a complement to the work of urban planners and public authorities.